借助文本区域增强的基于对象中心分层表示的自监督场景文本分割
Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions
AI总结：
针对文本分割像素级标签成本高、合成数据与真实场景分布差异大的问题，提出带区域查询模块和表示一致性约束的自监督场景文本分割算法，无需像素标注和合成预训练，性能优于现有无监督SOTA。
摘要：
文本分割任务具有十分广泛的应用价值，例如图像编辑、风格迁移、水印去除等。然而，现有公开数据集的像素级标签质量较差，且已被证实获取成本极高，在金钱和时间方面耗费巨大。同时，在合成数据集上进行预训练时，合成数据集的数据分布与真实场景中的数据分布相差甚远。这些都给当前的像素级文本分割算法带来了巨大挑战。为缓解上述问题，我们提出了一种自监督场景文本分割算法，该算法采用以对象为中心的方式得到的表示分层解耦，将图像分割为文本和背景。在我们的方法中，提出了两种新颖设计，包括适配文本独特属性的区域查询模块（Region Query Module）和表示一致性约束（Representation Consistency Constraints），作为自动编码器（Auto Encoder）的补充，提升了网络对文本的敏感度。针对这一独特设计，我们将文本定位模型预测的多边形级掩码作为额外输入信息，训练阶段既不使用任何像素级掩码标注，也不在合成数据集上进行预训练。大量实验证明了所提方法的有效性。在多个公开场景文本数据集上，我们的方法性能优于当前最优的无监督分割算法。
英文摘要：
Text segmentation tasks have a very wide range of application values, such as image editing, style transfer, watermark removal, etc.However, existing public datasets are of poor quality of pixel-level labels that have been shown to be notoriously costly to acquire, both in terms of money and time. At the same time, when pretraining is performed on synthetic datasets, the data distribution of the synthetic datasets is far from the data distribution in the real scene. These all pose a huge challenge to the current pixel-level text segmentation algorithms.To alleviate the above problems, we propose a self-supervised scene text segmentation algorithm with layered decoupling of representations derived from the object-centric manner to segment images into texts and background. In our method, we propose two novel designs which include Region Query Module and Representation Consistency Constraints adapting to the unique properties of text as complements to Auto Encoder, which improves the network's sensitivity to texts.For this unique design, we treat the polygon-level masks predicted by the text localization model as extra input information, and neither utilize any pixel-level mask annotations for training stage nor pretrain on synthetic datasets.Extensive experiments show the effectiveness of the method proposed. On several public scene text datasets, our method outperforms the state-of-the-art unsupervised segmentation algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
